Overview

Dataset statistics

Number of variables37
Number of observations9240
Missing cells41039
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory296.0 B

Variable types

Text1
Numeric6
Categorical16
Boolean14

Alerts

Magazine has constant value ""Constant
Receive More Updates About Our Courses has constant value ""Constant
Update me on Supply Chain Content has constant value ""Constant
Get updates on DM Content has constant value ""Constant
I agree to pay the amount through cheque has constant value ""Constant
TotalVisits is highly overall correlated with Total Time Spent on Website and 1 other fieldsHigh correlation
Total Time Spent on Website is highly overall correlated with TotalVisits and 1 other fieldsHigh correlation
Page Views Per Visit is highly overall correlated with TotalVisits and 1 other fieldsHigh correlation
Asymmetrique Activity Score is highly overall correlated with X Education Forums and 2 other fieldsHigh correlation
Asymmetrique Profile Score is highly overall correlated with X Education Forums and 2 other fieldsHigh correlation
Lead Origin is highly overall correlated with Lead Source and 1 other fieldsHigh correlation
Lead Source is highly overall correlated with Lead Origin and 1 other fieldsHigh correlation
Do Not Email is highly overall correlated with Last ActivityHigh correlation
Converted is highly overall correlated with Tags and 1 other fieldsHigh correlation
Last Activity is highly overall correlated with Do Not Email and 1 other fieldsHigh correlation
What is your current occupation is highly overall correlated with X Education ForumsHigh correlation
What matters most to you in choosing a course is highly overall correlated with X Education ForumsHigh correlation
X Education Forums is highly overall correlated with Asymmetrique Activity Score and 8 other fieldsHigh correlation
Newspaper is highly overall correlated with Asymmetrique Activity Score and 3 other fieldsHigh correlation
Tags is highly overall correlated with Converted and 2 other fieldsHigh correlation
Lead Quality is highly overall correlated with Converted and 2 other fieldsHigh correlation
Lead Profile is highly overall correlated with X Education ForumsHigh correlation
City is highly overall correlated with A free copy of Mastering The InterviewHigh correlation
Asymmetrique Activity Index is highly overall correlated with Asymmetrique Activity Score and 2 other fieldsHigh correlation
Asymmetrique Profile Index is highly overall correlated with Asymmetrique Profile Score and 2 other fieldsHigh correlation
A free copy of Mastering The Interview is highly overall correlated with Lead Origin and 2 other fieldsHigh correlation
Last Notable Activity is highly overall correlated with Last ActivityHigh correlation
Do Not Email is highly imbalanced (60.0%)Imbalance
Do Not Call is highly imbalanced (99.7%)Imbalance
Country is highly imbalanced (92.0%)Imbalance
How did you hear about X Education is highly imbalanced (53.0%)Imbalance
What is your current occupation is highly imbalanced (71.1%)Imbalance
What matters most to you in choosing a course is highly imbalanced (99.6%)Imbalance
Search is highly imbalanced (98.4%)Imbalance
Newspaper Article is highly imbalanced (99.7%)Imbalance
X Education Forums is highly imbalanced (99.8%)Imbalance
Newspaper is highly imbalanced (99.8%)Imbalance
Digital Advertisement is highly imbalanced (99.5%)Imbalance
Through Recommendations is highly imbalanced (99.1%)Imbalance
TotalVisits has 137 (1.5%) missing valuesMissing
Page Views Per Visit has 137 (1.5%) missing valuesMissing
Last Activity has 103 (1.1%) missing valuesMissing
Country has 2461 (26.6%) missing valuesMissing
Specialization has 1438 (15.6%) missing valuesMissing
How did you hear about X Education has 2207 (23.9%) missing valuesMissing
What is your current occupation has 2690 (29.1%) missing valuesMissing
What matters most to you in choosing a course has 2709 (29.3%) missing valuesMissing
Tags has 3353 (36.3%) missing valuesMissing
Lead Quality has 4767 (51.6%) missing valuesMissing
Lead Profile has 2709 (29.3%) missing valuesMissing
City has 1420 (15.4%) missing valuesMissing
Asymmetrique Activity Index has 4218 (45.6%) missing valuesMissing
Asymmetrique Profile Index has 4218 (45.6%) missing valuesMissing
Asymmetrique Activity Score has 4218 (45.6%) missing valuesMissing
Asymmetrique Profile Score has 4218 (45.6%) missing valuesMissing
Prospect ID has unique valuesUnique
Lead Number has unique valuesUnique
TotalVisits has 2189 (23.7%) zerosZeros
Total Time Spent on Website has 2193 (23.7%) zerosZeros
Page Views Per Visit has 2189 (23.7%) zerosZeros

Reproduction

Analysis started2023-07-16 23:54:50.860031
Analysis finished2023-07-16 23:55:00.001838
Duration9.14 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Prospect ID
Text

UNIQUE 

Distinct9240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
2023-07-16T16:55:00.113539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters332640
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9240 ?
Unique (%)100.0%

Sample

1st row7927b2df-8bba-4d29-b9a2-b6e0beafe620
2nd row2a272436-5132-4136-86fa-dcc88c88f482
3rd row8cc8c611-a219-4f35-ad23-fdfd2656bd8a
4th row0cc2df48-7cf4-4e39-9de9-19797f9b38cc
5th row3256f628-e534-4826-9d63-4a8b88782852
ValueCountFrequency (%)
7927b2df-8bba-4d29-b9a2-b6e0beafe620 1
 
< 0.1%
d9ed7525-5cf0-45ba-87c2-ca2bca521874 1
 
< 0.1%
30422ea1-36fe-465e-9e68-41ee190fefb3 1
 
< 0.1%
cfa0128c-a0da-4656-9d47-0aa4e67bf690 1
 
< 0.1%
8cc8c611-a219-4f35-ad23-fdfd2656bd8a 1
 
< 0.1%
0cc2df48-7cf4-4e39-9de9-19797f9b38cc 1
 
< 0.1%
3256f628-e534-4826-9d63-4a8b88782852 1
 
< 0.1%
2058ef08-2858-443e-a01f-a9237db2f5ce 1
 
< 0.1%
9fae7df4-169d-489b-afe4-0f3d752542ed 1
 
< 0.1%
20ef72a2-fb3b-45e0-924e-551c5fa59095 1
 
< 0.1%
Other values (9230) 9230
99.9%
2023-07-16T16:55:00.400270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 36960
 
11.1%
4 26383
 
7.9%
8 19749
 
5.9%
a 19646
 
5.9%
b 19453
 
5.8%
9 19396
 
5.8%
6 17607
 
5.3%
2 17574
 
5.3%
7 17542
 
5.3%
e 17482
 
5.3%
Other values (7) 120848
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 187432
56.3%
Lowercase Letter 108248
32.5%
Dash Punctuation 36960
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 26383
14.1%
8 19749
10.5%
9 19396
10.3%
6 17607
9.4%
2 17574
9.4%
7 17542
9.4%
0 17447
9.3%
5 17341
9.3%
1 17315
9.2%
3 17078
9.1%
Lowercase Letter
ValueCountFrequency (%)
a 19646
18.1%
b 19453
18.0%
e 17482
16.1%
d 17422
16.1%
f 17171
15.9%
c 17074
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 36960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 224392
67.5%
Latin 108248
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 36960
16.5%
4 26383
11.8%
8 19749
8.8%
9 19396
8.6%
6 17607
7.8%
2 17574
7.8%
7 17542
7.8%
0 17447
7.8%
5 17341
7.7%
1 17315
7.7%
Latin
ValueCountFrequency (%)
a 19646
18.1%
b 19453
18.0%
e 17482
16.1%
d 17422
16.1%
f 17171
15.9%
c 17074
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 36960
 
11.1%
4 26383
 
7.9%
8 19749
 
5.9%
a 19646
 
5.9%
b 19453
 
5.8%
9 19396
 
5.8%
6 17607
 
5.3%
2 17574
 
5.3%
7 17542
 
5.3%
e 17482
 
5.3%
Other values (7) 120848
36.3%

Lead Number
Real number (ℝ)

UNIQUE 

Distinct9240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617188.44
Minimum579533
Maximum660737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2023-07-16T16:55:00.554968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum579533
5-th percentile582869.9
Q1596484.5
median615479
Q3637387.25
95-th percentile655404.05
Maximum660737
Range81204
Interquartile range (IQR)40902.75

Descriptive statistics

Standard deviation23405.996
Coefficient of variation (CV)0.037923581
Kurtosis-1.2063933
Mean617188.44
Median Absolute Deviation (MAD)20413.5
Skewness0.14045109
Sum5.7028211 × 109
Variance5.4784063 × 108
MonotonicityNot monotonic
2023-07-16T16:55:00.716368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
660737 1
 
< 0.1%
603303 1
 
< 0.1%
602561 1
 
< 0.1%
602557 1
 
< 0.1%
602540 1
 
< 0.1%
602534 1
 
< 0.1%
602530 1
 
< 0.1%
602520 1
 
< 0.1%
602504 1
 
< 0.1%
602484 1
 
< 0.1%
Other values (9230) 9230
99.9%
ValueCountFrequency (%)
579533 1
< 0.1%
579538 1
< 0.1%
579545 1
< 0.1%
579546 1
< 0.1%
579564 1
< 0.1%
579615 1
< 0.1%
579622 1
< 0.1%
579642 1
< 0.1%
579697 1
< 0.1%
579701 1
< 0.1%
ValueCountFrequency (%)
660737 1
< 0.1%
660728 1
< 0.1%
660727 1
< 0.1%
660719 1
< 0.1%
660681 1
< 0.1%
660680 1
< 0.1%
660673 1
< 0.1%
660664 1
< 0.1%
660624 1
< 0.1%
660616 1
< 0.1%

Lead Origin
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
Landing Page Submission
4886 
API
3580 
Lead Add Form
718 
Lead Import
 
55
Quick Add Form
 
1

Length

Max length23
Median length23
Mean length14.401623
Min length3

Characters and Unicode

Total characters133071
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAPI
2nd rowAPI
3rd rowLanding Page Submission
4th rowLanding Page Submission
5th rowLanding Page Submission

Common Values

ValueCountFrequency (%)
Landing Page Submission 4886
52.9%
API 3580
38.7%
Lead Add Form 718
 
7.8%
Lead Import 55
 
0.6%
Quick Add Form 1
 
< 0.1%

Length

2023-07-16T16:55:00.873908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:00.989628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
landing 4886
23.8%
page 4886
23.8%
submission 4886
23.8%
api 3580
17.5%
lead 773
 
3.8%
add 719
 
3.5%
form 719
 
3.5%
import 55
 
0.3%
quick 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 14659
11.0%
n 14658
11.0%
11265
 
8.5%
a 10545
 
7.9%
g 9772
 
7.3%
s 9772
 
7.3%
P 8466
 
6.4%
d 7097
 
5.3%
m 5660
 
4.3%
o 5660
 
4.3%
Other values (14) 35517
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 94141
70.7%
Uppercase Letter 27665
 
20.8%
Space Separator 11265
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 14659
15.6%
n 14658
15.6%
a 10545
11.2%
g 9772
10.4%
s 9772
10.4%
d 7097
7.5%
m 5660
 
6.0%
o 5660
 
6.0%
e 5659
 
6.0%
u 4887
 
5.2%
Other values (6) 5772
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
P 8466
30.6%
L 5659
20.5%
S 4886
17.7%
A 4299
15.5%
I 3635
13.1%
F 719
 
2.6%
Q 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
11265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121806
91.5%
Common 11265
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 14659
12.0%
n 14658
12.0%
a 10545
 
8.7%
g 9772
 
8.0%
s 9772
 
8.0%
P 8466
 
7.0%
d 7097
 
5.8%
m 5660
 
4.6%
o 5660
 
4.6%
L 5659
 
4.6%
Other values (13) 29858
24.5%
Common
ValueCountFrequency (%)
11265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 14659
11.0%
n 14658
11.0%
11265
 
8.5%
a 10545
 
7.9%
g 9772
 
7.3%
s 9772
 
7.3%
P 8466
 
6.4%
d 7097
 
5.3%
m 5660
 
4.3%
o 5660
 
4.3%
Other values (14) 35517
26.7%

Lead Source
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing36
Missing (%)0.4%
Memory size72.3 KiB
Google
2868 
Direct Traffic
2543 
Olark Chat
1755 
Organic Search
1154 
Reference
534 
Other values (16)
350 

Length

Max length17
Median length16
Mean length10.432095
Min length4

Characters and Unicode

Total characters96017
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowOlark Chat
2nd rowOrganic Search
3rd rowDirect Traffic
4th rowDirect Traffic
5th rowGoogle

Common Values

ValueCountFrequency (%)
Google 2868
31.0%
Direct Traffic 2543
27.5%
Olark Chat 1755
19.0%
Organic Search 1154
12.5%
Reference 534
 
5.8%
Welingak Website 142
 
1.5%
Referral Sites 125
 
1.4%
Facebook 55
 
0.6%
bing 6
 
0.1%
google 5
 
0.1%
Other values (11) 17
 
0.2%
(Missing) 36
 
0.4%

Length

2023-07-16T16:55:01.118963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
google 2873
19.2%
direct 2543
17.0%
traffic 2543
17.0%
chat 1757
11.8%
olark 1755
11.8%
organic 1154
7.7%
search 1154
7.7%
reference 534
 
3.6%
welingak 142
 
1.0%
website 142
 
1.0%
Other values (19) 333
 
2.2%

Most occurring characters

ValueCountFrequency (%)
r 9938
 
10.4%
e 9584
 
10.0%
a 8699
 
9.1%
c 7995
 
8.3%
i 6666
 
6.9%
o 5863
 
6.1%
f 5745
 
6.0%
5726
 
6.0%
l 4916
 
5.1%
t 4570
 
4.8%
Other values (31) 26315
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75361
78.5%
Uppercase Letter 14922
 
15.5%
Space Separator 5726
 
6.0%
Decimal Number 4
 
< 0.1%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 9938
13.2%
e 9584
12.7%
a 8699
11.5%
c 7995
10.6%
i 6666
8.8%
o 5863
7.8%
f 5745
7.6%
l 4916
6.5%
t 4570
6.1%
g 4182
5.5%
Other values (12) 7203
9.6%
Uppercase Letter
ValueCountFrequency (%)
O 2909
19.5%
G 2868
19.2%
D 2544
17.0%
T 2543
17.0%
C 1763
11.8%
S 1281
8.6%
R 661
 
4.4%
W 285
 
1.9%
F 55
 
0.4%
P 3
 
< 0.1%
Other values (6) 10
 
0.1%
Space Separator
ValueCountFrequency (%)
5726
100.0%
Decimal Number
ValueCountFrequency (%)
2 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90283
94.0%
Common 5734
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 9938
11.0%
e 9584
10.6%
a 8699
 
9.6%
c 7995
 
8.9%
i 6666
 
7.4%
o 5863
 
6.5%
f 5745
 
6.4%
l 4916
 
5.4%
t 4570
 
5.1%
g 4182
 
4.6%
Other values (28) 22125
24.5%
Common
ValueCountFrequency (%)
5726
99.9%
2 4
 
0.1%
_ 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 9938
 
10.4%
e 9584
 
10.0%
a 8699
 
9.1%
c 7995
 
8.3%
i 6666
 
6.9%
o 5863
 
6.1%
f 5745
 
6.0%
5726
 
6.0%
l 4916
 
5.1%
t 4570
 
4.8%
Other values (31) 26315
27.4%

Do Not Email
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
8506 
True
 
734
ValueCountFrequency (%)
False 8506
92.1%
True 734
 
7.9%
2023-07-16T16:55:01.220607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Do Not Call
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9238 
True
 
2
ValueCountFrequency (%)
False 9238
> 99.9%
True 2
 
< 0.1%
2023-07-16T16:55:01.302360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Converted
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
0
5679 
1
3561 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Length

2023-07-16T16:55:01.402121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:01.493950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Most occurring characters

ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

TotalVisits
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct41
Distinct (%)0.5%
Missing137
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean3.4452378
Minimum0
Maximum251
Zeros2189
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2023-07-16T16:55:01.604657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum251
Range251
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.8548527
Coefficient of variation (CV)1.4091488
Kurtosis853.47871
Mean3.4452378
Median Absolute Deviation (MAD)2
Skewness19.911657
Sum31362
Variance23.569595
MonotonicityNot monotonic
2023-07-16T16:55:01.741366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 2189
23.7%
2 1680
18.2%
3 1306
14.1%
4 1120
12.1%
5 783
 
8.5%
6 466
 
5.0%
1 395
 
4.3%
7 309
 
3.3%
8 224
 
2.4%
9 164
 
1.8%
Other values (31) 467
 
5.1%
(Missing) 137
 
1.5%
ValueCountFrequency (%)
0 2189
23.7%
1 395
 
4.3%
2 1680
18.2%
3 1306
14.1%
4 1120
12.1%
5 783
 
8.5%
6 466
 
5.0%
7 309
 
3.3%
8 224
 
2.4%
9 164
 
1.8%
ValueCountFrequency (%)
251 1
< 0.1%
141 1
< 0.1%
115 1
< 0.1%
74 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
43 1
< 0.1%
42 1
< 0.1%
41 1
< 0.1%
32 1
< 0.1%

Total Time Spent on Website
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1731
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean487.69827
Minimum0
Maximum2272
Zeros2193
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2023-07-16T16:55:01.886107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median248
Q3936
95-th percentile1562
Maximum2272
Range2272
Interquartile range (IQR)924

Descriptive statistics

Standard deviation548.02147
Coefficient of variation (CV)1.1236896
Kurtosis-0.40376973
Mean487.69827
Median Absolute Deviation (MAD)248
Skewness0.95645019
Sum4506332
Variance300327.53
MonotonicityNot monotonic
2023-07-16T16:55:02.028725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2193
 
23.7%
60 19
 
0.2%
74 18
 
0.2%
75 18
 
0.2%
127 18
 
0.2%
157 17
 
0.2%
62 17
 
0.2%
234 17
 
0.2%
32 17
 
0.2%
87 17
 
0.2%
Other values (1721) 6889
74.6%
ValueCountFrequency (%)
0 2193
23.7%
1 7
 
0.1%
2 14
 
0.2%
3 9
 
0.1%
4 10
 
0.1%
5 13
 
0.1%
6 7
 
0.1%
7 8
 
0.1%
8 11
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
2272 1
< 0.1%
2253 1
< 0.1%
2226 1
< 0.1%
2217 1
< 0.1%
2207 1
< 0.1%
2170 1
< 0.1%
2140 1
< 0.1%
2137 1
< 0.1%
2125 1
< 0.1%
2117 1
< 0.1%

Page Views Per Visit
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct114
Distinct (%)1.3%
Missing137
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.3628199
Minimum0
Maximum55
Zeros2189
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2023-07-16T16:55:02.162368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum55
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1614178
Coefficient of variation (CV)0.91476194
Kurtosis42.362348
Mean2.3628199
Median Absolute Deviation (MAD)1
Skewness2.8717929
Sum21508.75
Variance4.6717267
MonotonicityNot monotonic
2023-07-16T16:55:02.301194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2189
23.7%
2 1795
19.4%
3 1196
12.9%
4 896
9.7%
1 651
 
7.0%
5 517
 
5.6%
1.5 306
 
3.3%
6 244
 
2.6%
2.5 241
 
2.6%
7 133
 
1.4%
Other values (104) 935
10.1%
(Missing) 137
 
1.5%
ValueCountFrequency (%)
0 2189
23.7%
1 651
 
7.0%
1.14 2
 
< 0.1%
1.17 1
 
< 0.1%
1.19 1
 
< 0.1%
1.2 5
 
0.1%
1.21 1
 
< 0.1%
1.22 2
 
< 0.1%
1.23 2
 
< 0.1%
1.25 23
 
0.2%
ValueCountFrequency (%)
55 1
 
< 0.1%
24 1
 
< 0.1%
16 3
 
< 0.1%
15 4
< 0.1%
14.5 1
 
< 0.1%
14 9
0.1%
13 6
0.1%
12.33 1
 
< 0.1%
12 5
0.1%
11.5 1
 
< 0.1%

Last Activity
Categorical

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)0.2%
Missing103
Missing (%)1.1%
Memory size72.3 KiB
Email Opened
3437 
SMS Sent
2745 
Olark Chat Conversation
973 
Page Visited on Website
640 
Converted to Lead
428 
Other values (12)
914 

Length

Max length28
Median length26
Mean length13.400241
Min length8

Characters and Unicode

Total characters122438
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPage Visited on Website
2nd rowEmail Opened
3rd rowEmail Opened
4th rowUnreachable
5th rowConverted to Lead

Common Values

ValueCountFrequency (%)
Email Opened 3437
37.2%
SMS Sent 2745
29.7%
Olark Chat Conversation 973
 
10.5%
Page Visited on Website 640
 
6.9%
Converted to Lead 428
 
4.6%
Email Bounced 326
 
3.5%
Email Link Clicked 267
 
2.9%
Form Submitted on Website 116
 
1.3%
Unreachable 93
 
1.0%
Unsubscribed 61
 
0.7%
Other values (7) 51
 
0.6%
(Missing) 103
 
1.1%

Length

2023-07-16T16:55:02.456367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
email 4034
18.9%
opened 3437
16.1%
sms 2745
12.8%
sent 2745
12.8%
conversation 1003
 
4.7%
olark 973
 
4.6%
chat 973
 
4.6%
on 756
 
3.5%
website 756
 
3.5%
visited 641
 
3.0%
Other values (26) 3320
15.5%

Most occurring characters

ValueCountFrequency (%)
e 15724
12.8%
12246
 
10.0%
n 10171
 
8.3%
S 8353
 
6.8%
a 8312
 
6.8%
i 7815
 
6.4%
t 7217
 
5.9%
d 5755
 
4.7%
l 5380
 
4.4%
O 4410
 
3.6%
Other values (28) 37055
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 84563
69.1%
Uppercase Letter 25629
 
20.9%
Space Separator 12246
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15724
18.6%
n 10171
12.0%
a 8312
9.8%
i 7815
9.2%
t 7217
8.5%
d 5755
 
6.8%
l 5380
 
6.4%
m 4269
 
5.0%
o 4118
 
4.9%
p 3466
 
4.1%
Other values (11) 12336
14.6%
Uppercase Letter
ValueCountFrequency (%)
S 8353
32.6%
O 4410
17.2%
E 4034
15.7%
M 2747
 
10.7%
C 2677
 
10.4%
W 756
 
2.9%
L 695
 
2.7%
P 670
 
2.6%
V 647
 
2.5%
B 327
 
1.3%
Other values (6) 313
 
1.2%
Space Separator
ValueCountFrequency (%)
12246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 110192
90.0%
Common 12246
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15724
14.3%
n 10171
 
9.2%
S 8353
 
7.6%
a 8312
 
7.5%
i 7815
 
7.1%
t 7217
 
6.5%
d 5755
 
5.2%
l 5380
 
4.9%
O 4410
 
4.0%
m 4269
 
3.9%
Other values (27) 32786
29.8%
Common
ValueCountFrequency (%)
12246
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15724
12.8%
12246
 
10.0%
n 10171
 
8.3%
S 8353
 
6.8%
a 8312
 
6.8%
i 7815
 
6.4%
t 7217
 
5.9%
d 5755
 
4.7%
l 5380
 
4.4%
O 4410
 
3.6%
Other values (28) 37055
30.3%

Country
Categorical

IMBALANCE  MISSING 

Distinct38
Distinct (%)0.6%
Missing2461
Missing (%)26.6%
Memory size72.3 KiB
India
6492 
United States
 
69
United Arab Emirates
 
53
Singapore
 
24
Saudi Arabia
 
21
Other values (33)
 
120

Length

Max length20
Median length5
Mean length5.291931
Min length4

Characters and Unicode

Total characters35874
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia

Common Values

ValueCountFrequency (%)
India 6492
70.3%
United States 69
 
0.7%
United Arab Emirates 53
 
0.6%
Singapore 24
 
0.3%
Saudi Arabia 21
 
0.2%
United Kingdom 15
 
0.2%
Australia 13
 
0.1%
Qatar 10
 
0.1%
Hong Kong 7
 
0.1%
Bahrain 7
 
0.1%
Other values (28) 68
 
0.7%
(Missing) 2461
 
26.6%

Length

2023-07-16T16:55:02.613974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india 6492
92.7%
united 137
 
2.0%
states 69
 
1.0%
arab 53
 
0.8%
emirates 53
 
0.8%
singapore 24
 
0.3%
saudi 21
 
0.3%
arabia 21
 
0.3%
kingdom 15
 
0.2%
australia 13
 
0.2%
Other values (35) 106
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 6891
19.2%
i 6826
19.0%
n 6750
18.8%
d 6680
18.6%
I 6495
18.1%
t 365
 
1.0%
e 321
 
0.9%
225
 
0.6%
r 205
 
0.6%
s 147
 
0.4%
Other values (35) 969
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28646
79.9%
Uppercase Letter 7001
 
19.5%
Space Separator 225
 
0.6%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6891
24.1%
i 6826
23.8%
n 6750
23.6%
d 6680
23.3%
t 365
 
1.3%
e 321
 
1.1%
r 205
 
0.7%
s 147
 
0.5%
m 82
 
0.3%
b 75
 
0.3%
Other values (12) 304
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
I 6495
92.8%
U 139
 
2.0%
S 123
 
1.8%
A 93
 
1.3%
E 53
 
0.8%
K 27
 
0.4%
B 11
 
0.2%
Q 10
 
0.1%
H 7
 
0.1%
F 6
 
0.1%
Other values (11) 37
 
0.5%
Space Separator
ValueCountFrequency (%)
225
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35647
99.4%
Common 227
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6891
19.3%
i 6826
19.1%
n 6750
18.9%
d 6680
18.7%
I 6495
18.2%
t 365
 
1.0%
e 321
 
0.9%
r 205
 
0.6%
s 147
 
0.4%
U 139
 
0.4%
Other values (33) 828
 
2.3%
Common
ValueCountFrequency (%)
225
99.1%
/ 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6891
19.2%
i 6826
19.0%
n 6750
18.8%
d 6680
18.6%
I 6495
18.1%
t 365
 
1.0%
e 321
 
0.9%
225
 
0.6%
r 205
 
0.6%
s 147
 
0.4%
Other values (35) 969
 
2.7%

Specialization
Categorical

MISSING 

Distinct19
Distinct (%)0.2%
Missing1438
Missing (%)15.6%
Memory size72.3 KiB
Select
1942 
Finance Management
976 
Human Resource Management
848 
Marketing Management
838 
Operations Management
503 
Other values (14)
2695 

Length

Max length33
Median length23
Mean length17.646885
Min length6

Characters and Unicode

Total characters137681
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowBusiness Administration
4th rowMedia and Advertising
5th rowSelect

Common Values

ValueCountFrequency (%)
Select 1942
21.0%
Finance Management 976
10.6%
Human Resource Management 848
9.2%
Marketing Management 838
9.1%
Operations Management 503
 
5.4%
Business Administration 403
 
4.4%
IT Projects Management 366
 
4.0%
Supply Chain Management 349
 
3.8%
Banking, Investment And Insurance 338
 
3.7%
Media and Advertising 203
 
2.2%
Other values (9) 1036
11.2%
(Missing) 1438
15.6%

Length

2023-07-16T16:55:02.749609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
management 4253
26.2%
select 1942
12.0%
finance 976
 
6.0%
human 848
 
5.2%
resource 848
 
5.2%
marketing 838
 
5.2%
and 817
 
5.0%
business 581
 
3.6%
operations 503
 
3.1%
administration 403
 
2.5%
Other values (21) 4202
25.9%

Most occurring characters

ValueCountFrequency (%)
e 19899
14.5%
n 18135
13.2%
a 14945
10.9%
t 10430
 
7.6%
8409
 
6.1%
i 6355
 
4.6%
m 6045
 
4.4%
g 5705
 
4.1%
M 5518
 
4.0%
s 5489
 
4.0%
Other values (28) 36751
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111714
81.1%
Uppercase Letter 17051
 
12.4%
Space Separator 8409
 
6.1%
Other Punctuation 338
 
0.2%
Dash Punctuation 169
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 19899
17.8%
n 18135
16.2%
a 14945
13.4%
t 10430
9.3%
i 6355
 
5.7%
m 6045
 
5.4%
g 5705
 
5.1%
s 5489
 
4.9%
c 4749
 
4.3%
r 4428
 
4.0%
Other values (12) 15534
13.9%
Uppercase Letter
ValueCountFrequency (%)
M 5518
32.4%
S 2331
13.7%
I 1220
 
7.2%
R 1133
 
6.6%
H 1121
 
6.6%
A 1017
 
6.0%
B 976
 
5.7%
F 976
 
5.7%
T 772
 
4.5%
O 615
 
3.6%
Other values (3) 1372
 
8.0%
Space Separator
ValueCountFrequency (%)
8409
100.0%
Other Punctuation
ValueCountFrequency (%)
, 338
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 128765
93.5%
Common 8916
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 19899
15.5%
n 18135
14.1%
a 14945
11.6%
t 10430
 
8.1%
i 6355
 
4.9%
m 6045
 
4.7%
g 5705
 
4.4%
M 5518
 
4.3%
s 5489
 
4.3%
c 4749
 
3.7%
Other values (25) 31495
24.5%
Common
ValueCountFrequency (%)
8409
94.3%
, 338
 
3.8%
- 169
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137681
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 19899
14.5%
n 18135
13.2%
a 14945
10.9%
t 10430
 
7.6%
8409
 
6.1%
i 6355
 
4.6%
m 6045
 
4.4%
g 5705
 
4.1%
M 5518
 
4.0%
s 5489
 
4.0%
Other values (28) 36751
26.7%

How did you hear about X Education
Categorical

IMBALANCE  MISSING 

Distinct10
Distinct (%)0.1%
Missing2207
Missing (%)23.9%
Memory size72.3 KiB
Select
5043 
Online Search
808 
Word Of Mouth
 
348
Student of SomeSchool
 
310
Other
 
186
Other values (5)
 
338

Length

Max length21
Median length6
Mean length8.1246979
Min length3

Characters and Unicode

Total characters57141
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowSelect
4th rowWord Of Mouth
5th rowOther

Common Values

ValueCountFrequency (%)
Select 5043
54.6%
Online Search 808
 
8.7%
Word Of Mouth 348
 
3.8%
Student of SomeSchool 310
 
3.4%
Other 186
 
2.0%
Multiple Sources 152
 
1.6%
Advertisements 70
 
0.8%
Social Media 67
 
0.7%
Email 26
 
0.3%
SMS 23
 
0.2%
(Missing) 2207
23.9%

Length

2023-07-16T16:55:02.883546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:03.018207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
select 5043
53.8%
online 808
 
8.6%
search 808
 
8.6%
of 658
 
7.0%
word 348
 
3.7%
mouth 348
 
3.7%
student 310
 
3.3%
someschool 310
 
3.3%
other 186
 
2.0%
multiple 152
 
1.6%
Other values (6) 405
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 13089
22.9%
S 7046
12.3%
l 6558
11.5%
t 6489
11.4%
c 6380
11.2%
2343
 
4.1%
o 2155
 
3.8%
n 1996
 
3.5%
h 1652
 
2.9%
r 1564
 
2.7%
Other values (14) 7869
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45376
79.4%
Uppercase Letter 9422
 
16.5%
Space Separator 2343
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13089
28.8%
l 6558
14.5%
t 6489
14.3%
c 6380
14.1%
o 2155
 
4.7%
n 1996
 
4.4%
h 1652
 
3.6%
r 1564
 
3.4%
i 1190
 
2.6%
a 968
 
2.1%
Other values (7) 3335
 
7.3%
Uppercase Letter
ValueCountFrequency (%)
S 7046
74.8%
O 1342
 
14.2%
M 590
 
6.3%
W 348
 
3.7%
A 70
 
0.7%
E 26
 
0.3%
Space Separator
ValueCountFrequency (%)
2343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54798
95.9%
Common 2343
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13089
23.9%
S 7046
12.9%
l 6558
12.0%
t 6489
11.8%
c 6380
11.6%
o 2155
 
3.9%
n 1996
 
3.6%
h 1652
 
3.0%
r 1564
 
2.9%
O 1342
 
2.4%
Other values (13) 6527
11.9%
Common
ValueCountFrequency (%)
2343
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13089
22.9%
S 7046
12.3%
l 6558
11.5%
t 6489
11.4%
c 6380
11.2%
2343
 
4.1%
o 2155
 
3.8%
n 1996
 
3.5%
h 1652
 
2.9%
r 1564
 
2.7%
Other values (14) 7869
13.8%

What is your current occupation
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)0.1%
Missing2690
Missing (%)29.1%
Memory size72.3 KiB
Unemployed
5600 
Working Professional
706 
Student
 
210
Other
 
16
Housewife
 
10

Length

Max length20
Median length10
Mean length10.96916
Min length5

Characters and Unicode

Total characters71848
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnemployed
2nd rowUnemployed
3rd rowStudent
4th rowUnemployed
5th rowUnemployed

Common Values

ValueCountFrequency (%)
Unemployed 5600
60.6%
Working Professional 706
 
7.6%
Student 210
 
2.3%
Other 16
 
0.2%
Housewife 10
 
0.1%
Businessman 8
 
0.1%
(Missing) 2690
29.1%

Length

2023-07-16T16:55:03.180599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:03.302959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
unemployed 5600
77.2%
working 706
 
9.7%
professional 706
 
9.7%
student 210
 
2.9%
other 16
 
0.2%
housewife 10
 
0.1%
businessman 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 12160
16.9%
o 7728
10.8%
n 7238
10.1%
l 6306
8.8%
d 5810
8.1%
m 5608
7.8%
U 5600
7.8%
p 5600
7.8%
y 5600
7.8%
s 1446
 
2.0%
Other values (17) 8752
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 63886
88.9%
Uppercase Letter 7256
 
10.1%
Space Separator 706
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12160
19.0%
o 7728
12.1%
n 7238
11.3%
l 6306
9.9%
d 5810
9.1%
m 5608
8.8%
p 5600
8.8%
y 5600
8.8%
s 1446
 
2.3%
i 1430
 
2.2%
Other values (9) 4960
7.8%
Uppercase Letter
ValueCountFrequency (%)
U 5600
77.2%
P 706
 
9.7%
W 706
 
9.7%
S 210
 
2.9%
O 16
 
0.2%
H 10
 
0.1%
B 8
 
0.1%
Space Separator
ValueCountFrequency (%)
706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71142
99.0%
Common 706
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12160
17.1%
o 7728
10.9%
n 7238
10.2%
l 6306
8.9%
d 5810
8.2%
m 5608
7.9%
U 5600
7.9%
p 5600
7.9%
y 5600
7.9%
s 1446
 
2.0%
Other values (16) 8046
11.3%
Common
ValueCountFrequency (%)
706
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12160
16.9%
o 7728
10.8%
n 7238
10.1%
l 6306
8.8%
d 5810
8.1%
m 5608
7.8%
U 5600
7.8%
p 5600
7.8%
y 5600
7.8%
s 1446
 
2.0%
Other values (17) 8752
12.2%

What matters most to you in choosing a course
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2709
Missing (%)29.3%
Memory size72.3 KiB
Better Career Prospects
6528 
Flexibility & Convenience
 
2
Other
 
1

Length

Max length25
Median length23
Mean length22.997856
Min length5

Characters and Unicode

Total characters150199
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBetter Career Prospects
2nd rowBetter Career Prospects
3rd rowBetter Career Prospects
4th rowBetter Career Prospects
5th rowBetter Career Prospects

Common Values

ValueCountFrequency (%)
Better Career Prospects 6528
70.6%
Flexibility & Convenience 2
 
< 0.1%
Other 1
 
< 0.1%
(Missing) 2709
29.3%

Length

2023-07-16T16:55:03.450564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:03.570277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
better 6528
33.3%
career 6528
33.3%
prospects 6528
33.3%
flexibility 2
 
< 0.1%
2
 
< 0.1%
convenience 2
 
< 0.1%
other 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 32649
21.7%
r 26113
17.4%
t 19587
13.0%
13060
8.7%
s 13056
 
8.7%
c 6530
 
4.3%
C 6530
 
4.3%
o 6530
 
4.3%
p 6528
 
4.3%
B 6528
 
4.3%
Other values (13) 13088
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 117548
78.3%
Uppercase Letter 19589
 
13.0%
Space Separator 13060
 
8.7%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 32649
27.8%
r 26113
22.2%
t 19587
16.7%
s 13056
 
11.1%
c 6530
 
5.6%
o 6530
 
5.6%
p 6528
 
5.6%
a 6528
 
5.6%
i 8
 
< 0.1%
n 6
 
< 0.1%
Other values (6) 13
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 6530
33.3%
B 6528
33.3%
P 6528
33.3%
F 2
 
< 0.1%
O 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
13060
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 137137
91.3%
Common 13062
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 32649
23.8%
r 26113
19.0%
t 19587
14.3%
s 13056
 
9.5%
c 6530
 
4.8%
C 6530
 
4.8%
o 6530
 
4.8%
p 6528
 
4.8%
B 6528
 
4.8%
P 6528
 
4.8%
Other values (11) 6558
 
4.8%
Common
ValueCountFrequency (%)
13060
> 99.9%
& 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 32649
21.7%
r 26113
17.4%
t 19587
13.0%
13060
8.7%
s 13056
 
8.7%
c 6530
 
4.3%
C 6530
 
4.3%
o 6530
 
4.3%
p 6528
 
4.3%
B 6528
 
4.3%
Other values (13) 13088
8.7%

Search
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9226 
True
 
14
ValueCountFrequency (%)
False 9226
99.8%
True 14
 
0.2%
2023-07-16T16:55:03.664022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Magazine
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2023-07-16T16:55:03.740815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Newspaper Article
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9238 
True
 
2
ValueCountFrequency (%)
False 9238
> 99.9%
True 2
 
< 0.1%
2023-07-16T16:55:03.819577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

X Education Forums
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9239 
True
 
1
ValueCountFrequency (%)
False 9239
> 99.9%
True 1
 
< 0.1%
2023-07-16T16:55:03.901355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Newspaper
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9239 
True
 
1
ValueCountFrequency (%)
False 9239
> 99.9%
True 1
 
< 0.1%
2023-07-16T16:55:03.981168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Digital Advertisement
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9236 
True
 
4
ValueCountFrequency (%)
False 9236
> 99.9%
True 4
 
< 0.1%
2023-07-16T16:55:04.062386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Through Recommendations
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9233 
True
 
7
ValueCountFrequency (%)
False 9233
99.9%
True 7
 
0.1%
2023-07-16T16:55:04.140151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2023-07-16T16:55:04.217515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Tags
Categorical

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)0.4%
Missing3353
Missing (%)36.3%
Memory size72.3 KiB
Will revert after reading the email
2072 
Ringing
1203 
Interested in other courses
513 
Already a student
465 
Closed by Horizzon
358 
Other values (21)
1276 

Length

Max length49
Median length37
Mean length22.145405
Min length4

Characters and Unicode

Total characters130370
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowInterested in other courses
2nd rowRinging
3rd rowWill revert after reading the email
4th rowRinging
5th rowWill revert after reading the email

Common Values

ValueCountFrequency (%)
Will revert after reading the email 2072
22.4%
Ringing 1203
 
13.0%
Interested in other courses 513
 
5.6%
Already a student 465
 
5.0%
Closed by Horizzon 358
 
3.9%
switched off 240
 
2.6%
Busy 186
 
2.0%
Lost to EINS 175
 
1.9%
Not doing further education 145
 
1.6%
Interested in full time MBA 117
 
1.3%
Other values (16) 413
 
4.5%
(Missing) 3353
36.3%

Length

2023-07-16T16:55:04.434905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 2074
 
9.5%
will 2072
 
9.5%
email 2072
 
9.5%
revert 2072
 
9.5%
reading 2072
 
9.5%
after 2072
 
9.5%
ringing 1203
 
5.5%
in 765
 
3.5%
interested 635
 
2.9%
courses 513
 
2.4%
Other values (61) 6215
28.6%

Most occurring characters

ValueCountFrequency (%)
e 17825
13.7%
15995
12.3%
r 11725
 
9.0%
i 11002
 
8.4%
t 10394
 
8.0%
a 7750
 
5.9%
l 7640
 
5.9%
n 7559
 
5.8%
g 4935
 
3.8%
d 4847
 
3.7%
Other values (35) 30698
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107183
82.2%
Space Separator 15995
 
12.3%
Uppercase Letter 7064
 
5.4%
Open Punctuation 64
 
< 0.1%
Close Punctuation 64
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17825
16.6%
r 11725
10.9%
i 11002
10.3%
t 10394
9.7%
a 7750
 
7.2%
l 7640
 
7.1%
n 7559
 
7.1%
g 4935
 
4.6%
d 4847
 
4.5%
o 3740
 
3.5%
Other values (14) 19766
18.4%
Uppercase Letter
ValueCountFrequency (%)
W 2078
29.4%
R 1204
17.0%
I 827
 
11.7%
A 582
 
8.2%
N 400
 
5.7%
C 359
 
5.1%
H 358
 
5.1%
B 303
 
4.3%
E 251
 
3.6%
S 195
 
2.8%
Other values (8) 507
 
7.2%
Space Separator
ValueCountFrequency (%)
15995
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 114247
87.6%
Common 16123
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17825
15.6%
r 11725
10.3%
i 11002
9.6%
t 10394
 
9.1%
a 7750
 
6.8%
l 7640
 
6.7%
n 7559
 
6.6%
g 4935
 
4.3%
d 4847
 
4.2%
o 3740
 
3.3%
Other values (32) 26830
23.5%
Common
ValueCountFrequency (%)
15995
99.2%
( 64
 
0.4%
) 64
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17825
13.7%
15995
12.3%
r 11725
 
9.0%
i 11002
 
8.4%
t 10394
 
8.0%
a 7750
 
5.9%
l 7640
 
5.9%
n 7559
 
5.8%
g 4935
 
3.8%
d 4847
 
3.7%
Other values (35) 30698
23.5%

Lead Quality
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.1%
Missing4767
Missing (%)51.6%
Memory size72.3 KiB
Might be
1560 
Not Sure
1092 
High in Relevance
637 
Worst
601 
Low in Relevance
583 

Length

Max length17
Median length8
Mean length9.9213056
Min length5

Characters and Unicode

Total characters44378
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow in Relevance
2nd rowMight be
3rd rowNot Sure
4th rowMight be
5th rowLow in Relevance

Common Values

ValueCountFrequency (%)
Might be 1560
 
16.9%
Not Sure 1092
 
11.8%
High in Relevance 637
 
6.9%
Worst 601
 
6.5%
Low in Relevance 583
 
6.3%
(Missing) 4767
51.6%

Length

2023-07-16T16:55:04.558459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:04.674146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
might 1560
16.3%
be 1560
16.3%
in 1220
12.8%
relevance 1220
12.8%
not 1092
11.4%
sure 1092
11.4%
high 637
6.7%
worst 601
 
6.3%
low 583
 
6.1%

Most occurring characters

ValueCountFrequency (%)
e 6312
14.2%
5092
 
11.5%
i 3417
 
7.7%
t 3253
 
7.3%
n 2440
 
5.5%
o 2276
 
5.1%
g 2197
 
5.0%
h 2197
 
5.0%
r 1693
 
3.8%
M 1560
 
3.5%
Other values (14) 13941
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32501
73.2%
Uppercase Letter 6785
 
15.3%
Space Separator 5092
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6312
19.4%
i 3417
10.5%
t 3253
10.0%
n 2440
 
7.5%
o 2276
 
7.0%
g 2197
 
6.8%
h 2197
 
6.8%
r 1693
 
5.2%
b 1560
 
4.8%
l 1220
 
3.8%
Other values (6) 5936
18.3%
Uppercase Letter
ValueCountFrequency (%)
M 1560
23.0%
R 1220
18.0%
S 1092
16.1%
N 1092
16.1%
H 637
9.4%
W 601
 
8.9%
L 583
 
8.6%
Space Separator
ValueCountFrequency (%)
5092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39286
88.5%
Common 5092
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6312
16.1%
i 3417
 
8.7%
t 3253
 
8.3%
n 2440
 
6.2%
o 2276
 
5.8%
g 2197
 
5.6%
h 2197
 
5.6%
r 1693
 
4.3%
M 1560
 
4.0%
b 1560
 
4.0%
Other values (13) 12381
31.5%
Common
ValueCountFrequency (%)
5092
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6312
14.2%
5092
 
11.5%
i 3417
 
7.7%
t 3253
 
7.3%
n 2440
 
5.5%
o 2276
 
5.1%
g 2197
 
5.0%
h 2197
 
5.0%
r 1693
 
3.8%
M 1560
 
3.5%
Other values (14) 13941
31.4%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2023-07-16T16:55:04.780462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Get updates on DM Content
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2023-07-16T16:55:04.849819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Lead Profile
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.1%
Missing2709
Missing (%)29.3%
Memory size72.3 KiB
Select
4146 
Potential Lead
1613 
Other Leads
487 
Student of SomeSchool
 
241
Lateral Student
 
24

Length

Max length27
Median length6
Mean length8.9995407
Min length6

Characters and Unicode

Total characters58776
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowPotential Lead
4th rowSelect
5th rowSelect

Common Values

ValueCountFrequency (%)
Select 4146
44.9%
Potential Lead 1613
 
17.5%
Other Leads 487
 
5.3%
Student of SomeSchool 241
 
2.6%
Lateral Student 24
 
0.3%
Dual Specialization Student 20
 
0.2%
(Missing) 2709
29.3%

Length

2023-07-16T16:55:04.946589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:05.057264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
select 4146
45.2%
potential 1613
 
17.6%
lead 1613
 
17.6%
other 487
 
5.3%
leads 487
 
5.3%
student 285
 
3.1%
of 241
 
2.6%
someschool 241
 
2.6%
lateral 24
 
0.3%
dual 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 13062
22.2%
t 8473
14.4%
l 6064
10.3%
S 4933
 
8.4%
c 4407
 
7.5%
a 3821
 
6.5%
2646
 
4.5%
o 2597
 
4.4%
d 2385
 
4.1%
L 2124
 
3.6%
Other values (13) 8264
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46953
79.9%
Uppercase Letter 9177
 
15.6%
Space Separator 2646
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13062
27.8%
t 8473
18.0%
l 6064
12.9%
c 4407
 
9.4%
a 3821
 
8.1%
o 2597
 
5.5%
d 2385
 
5.1%
n 1918
 
4.1%
i 1673
 
3.6%
h 728
 
1.6%
Other values (7) 1825
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
S 4933
53.8%
L 2124
23.1%
P 1613
 
17.6%
O 487
 
5.3%
D 20
 
0.2%
Space Separator
ValueCountFrequency (%)
2646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56130
95.5%
Common 2646
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13062
23.3%
t 8473
15.1%
l 6064
10.8%
S 4933
 
8.8%
c 4407
 
7.9%
a 3821
 
6.8%
o 2597
 
4.6%
d 2385
 
4.2%
L 2124
 
3.8%
n 1918
 
3.4%
Other values (12) 6346
11.3%
Common
ValueCountFrequency (%)
2646
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13062
22.2%
t 8473
14.4%
l 6064
10.3%
S 4933
 
8.4%
c 4407
 
7.5%
a 3821
 
6.5%
2646
 
4.5%
o 2597
 
4.4%
d 2385
 
4.1%
L 2124
 
3.6%
Other values (13) 8264
14.1%

City
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.1%
Missing1420
Missing (%)15.4%
Memory size72.3 KiB
Mumbai
3222 
Select
2249 
Thane & Outskirts
752 
Other Cities
686 
Other Cities of Maharashtra
457 
Other values (2)
454 

Length

Max length27
Median length6
Mean length9.4702046
Min length6

Characters and Unicode

Total characters74057
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowMumbai
4th rowMumbai
5th rowMumbai

Common Values

ValueCountFrequency (%)
Mumbai 3222
34.9%
Select 2249
24.3%
Thane & Outskirts 752
 
8.1%
Other Cities 686
 
7.4%
Other Cities of Maharashtra 457
 
4.9%
Other Metro Cities 380
 
4.1%
Tier II Cities 74
 
0.8%
(Missing) 1420
15.4%

Length

2023-07-16T16:55:05.196068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:05.316537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mumbai 3222
26.2%
select 2249
18.3%
cities 1597
13.0%
other 1523
12.4%
thane 752
 
6.1%
752
 
6.1%
outskirts 752
 
6.1%
of 457
 
3.7%
maharashtra 457
 
3.7%
metro 380
 
3.1%
Other values (2) 148
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 8824
11.9%
t 7710
 
10.4%
i 7242
 
9.8%
a 5802
 
7.8%
4469
 
6.0%
M 4059
 
5.5%
u 3974
 
5.4%
r 3643
 
4.9%
s 3558
 
4.8%
m 3222
 
4.4%
Other values (14) 21554
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57682
77.9%
Uppercase Letter 11154
 
15.1%
Space Separator 4469
 
6.0%
Other Punctuation 752
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8824
15.3%
t 7710
13.4%
i 7242
12.6%
a 5802
10.1%
u 3974
6.9%
r 3643
6.3%
s 3558
6.2%
m 3222
 
5.6%
b 3222
 
5.6%
h 3189
 
5.5%
Other values (6) 7296
12.6%
Uppercase Letter
ValueCountFrequency (%)
M 4059
36.4%
O 2275
20.4%
S 2249
20.2%
C 1597
 
14.3%
T 826
 
7.4%
I 148
 
1.3%
Space Separator
ValueCountFrequency (%)
4469
100.0%
Other Punctuation
ValueCountFrequency (%)
& 752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68836
93.0%
Common 5221
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8824
12.8%
t 7710
11.2%
i 7242
10.5%
a 5802
 
8.4%
M 4059
 
5.9%
u 3974
 
5.8%
r 3643
 
5.3%
s 3558
 
5.2%
m 3222
 
4.7%
b 3222
 
4.7%
Other values (12) 17580
25.5%
Common
ValueCountFrequency (%)
4469
85.6%
& 752
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8824
11.9%
t 7710
 
10.4%
i 7242
 
9.8%
a 5802
 
7.8%
4469
 
6.0%
M 4059
 
5.5%
u 3974
 
5.4%
r 3643
 
4.9%
s 3558
 
4.8%
m 3222
 
4.4%
Other values (14) 21554
29.1%

Asymmetrique Activity Index
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.1%
Missing4218
Missing (%)45.6%
Memory size72.3 KiB
02.Medium
3839 
01.High
821 
03.Low
 
362

Length

Max length9
Median length9
Mean length8.4567901
Min length6

Characters and Unicode

Total characters42470
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02.Medium
2nd row02.Medium
3rd row02.Medium
4th row02.Medium
5th row02.Medium

Common Values

ValueCountFrequency (%)
02.Medium 3839
41.5%
01.High 821
 
8.9%
03.Low 362
 
3.9%
(Missing) 4218
45.6%

Length

2023-07-16T16:55:05.466135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:05.570827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
02.medium 3839
76.4%
01.high 821
 
16.3%
03.low 362
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 5022
11.8%
. 5022
11.8%
i 4660
11.0%
2 3839
9.0%
u 3839
9.0%
m 3839
9.0%
d 3839
9.0%
e 3839
9.0%
M 3839
9.0%
1 821
 
1.9%
Other values (7) 3911
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22382
52.7%
Decimal Number 10044
23.6%
Other Punctuation 5022
 
11.8%
Uppercase Letter 5022
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4660
20.8%
u 3839
17.2%
m 3839
17.2%
d 3839
17.2%
e 3839
17.2%
g 821
 
3.7%
h 821
 
3.7%
o 362
 
1.6%
w 362
 
1.6%
Decimal Number
ValueCountFrequency (%)
0 5022
50.0%
2 3839
38.2%
1 821
 
8.2%
3 362
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
M 3839
76.4%
H 821
 
16.3%
L 362
 
7.2%
Other Punctuation
ValueCountFrequency (%)
. 5022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27404
64.5%
Common 15066
35.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4660
17.0%
u 3839
14.0%
m 3839
14.0%
d 3839
14.0%
e 3839
14.0%
M 3839
14.0%
H 821
 
3.0%
g 821
 
3.0%
h 821
 
3.0%
L 362
 
1.3%
Other values (2) 724
 
2.6%
Common
ValueCountFrequency (%)
0 5022
33.3%
. 5022
33.3%
2 3839
25.5%
1 821
 
5.4%
3 362
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5022
11.8%
. 5022
11.8%
i 4660
11.0%
2 3839
9.0%
u 3839
9.0%
m 3839
9.0%
d 3839
9.0%
e 3839
9.0%
M 3839
9.0%
1 821
 
1.9%
Other values (7) 3911
9.2%

Asymmetrique Profile Index
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.1%
Missing4218
Missing (%)45.6%
Memory size72.3 KiB
02.Medium
2788 
01.High
2203 
03.Low
 
31

Length

Max length9
Median length9
Mean length8.1041418
Min length6

Characters and Unicode

Total characters40699
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02.Medium
2nd row02.Medium
3rd row01.High
4th row01.High
5th row01.High

Common Values

ValueCountFrequency (%)
02.Medium 2788
30.2%
01.High 2203
23.8%
03.Low 31
 
0.3%
(Missing) 4218
45.6%

Length

2023-07-16T16:55:05.688553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T16:55:05.794225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
02.medium 2788
55.5%
01.high 2203
43.9%
03.low 31
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 5022
12.3%
. 5022
12.3%
i 4991
12.3%
2 2788
6.9%
u 2788
6.9%
m 2788
6.9%
d 2788
6.9%
e 2788
6.9%
M 2788
6.9%
1 2203
 
5.4%
Other values (7) 6733
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20611
50.6%
Decimal Number 10044
24.7%
Other Punctuation 5022
 
12.3%
Uppercase Letter 5022
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4991
24.2%
u 2788
13.5%
m 2788
13.5%
d 2788
13.5%
e 2788
13.5%
g 2203
10.7%
h 2203
10.7%
o 31
 
0.2%
w 31
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 5022
50.0%
2 2788
27.8%
1 2203
21.9%
3 31
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
M 2788
55.5%
H 2203
43.9%
L 31
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 5022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25633
63.0%
Common 15066
37.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4991
19.5%
u 2788
10.9%
m 2788
10.9%
d 2788
10.9%
e 2788
10.9%
M 2788
10.9%
H 2203
8.6%
g 2203
8.6%
h 2203
8.6%
L 31
 
0.1%
Other values (2) 62
 
0.2%
Common
ValueCountFrequency (%)
0 5022
33.3%
. 5022
33.3%
2 2788
18.5%
1 2203
14.6%
3 31
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5022
12.3%
. 5022
12.3%
i 4991
12.3%
2 2788
6.9%
u 2788
6.9%
m 2788
6.9%
d 2788
6.9%
e 2788
6.9%
M 2788
6.9%
1 2203
 
5.4%
Other values (7) 6733
16.5%

Asymmetrique Activity Score
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)0.2%
Missing4218
Missing (%)45.6%
Infinite0
Infinite (%)0.0%
Mean14.306252
Minimum7
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2023-07-16T16:55:05.897947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q114
median14
Q315
95-th percentile17
Maximum18
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3866941
Coefficient of variation (CV)0.096929233
Kurtosis1.2330856
Mean14.306252
Median Absolute Deviation (MAD)1
Skewness-0.3833797
Sum71846
Variance1.9229205
MonotonicityNot monotonic
2023-07-16T16:55:06.012613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
14 1771
19.2%
15 1293
 
14.0%
13 775
 
8.4%
16 467
 
5.1%
17 349
 
3.8%
12 196
 
2.1%
11 95
 
1.0%
10 57
 
0.6%
9 9
 
0.1%
18 5
 
0.1%
Other values (2) 5
 
0.1%
(Missing) 4218
45.6%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 4
 
< 0.1%
9 9
 
0.1%
10 57
 
0.6%
11 95
 
1.0%
12 196
 
2.1%
13 775
8.4%
14 1771
19.2%
15 1293
14.0%
16 467
 
5.1%
ValueCountFrequency (%)
18 5
 
0.1%
17 349
 
3.8%
16 467
 
5.1%
15 1293
14.0%
14 1771
19.2%
13 775
8.4%
12 196
 
2.1%
11 95
 
1.0%
10 57
 
0.6%
9 9
 
0.1%

Asymmetrique Profile Score
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)0.2%
Missing4218
Missing (%)45.6%
Infinite0
Infinite (%)0.0%
Mean16.344883
Minimum11
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2023-07-16T16:55:06.125344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile14
Q115
median16
Q318
95-th percentile20
Maximum20
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.811395
Coefficient of variation (CV)0.11082337
Kurtosis-0.61973145
Mean16.344883
Median Absolute Deviation (MAD)1
Skewness0.22173872
Sum82084
Variance3.2811519
MonotonicityNot monotonic
2023-07-16T16:55:06.233051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
15 1759
19.0%
18 1071
 
11.6%
16 599
 
6.5%
17 579
 
6.3%
20 308
 
3.3%
19 245
 
2.7%
14 226
 
2.4%
13 204
 
2.2%
12 22
 
0.2%
11 9
 
0.1%
(Missing) 4218
45.6%
ValueCountFrequency (%)
11 9
 
0.1%
12 22
 
0.2%
13 204
 
2.2%
14 226
 
2.4%
15 1759
19.0%
16 599
 
6.5%
17 579
 
6.3%
18 1071
11.6%
19 245
 
2.7%
20 308
 
3.3%
ValueCountFrequency (%)
20 308
 
3.3%
19 245
 
2.7%
18 1071
11.6%
17 579
 
6.3%
16 599
 
6.5%
15 1759
19.0%
14 226
 
2.4%
13 204
 
2.2%
12 22
 
0.2%
11 9
 
0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2023-07-16T16:55:06.325634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

A free copy of Mastering The Interview
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
6352 
True
2888 
ValueCountFrequency (%)
False 6352
68.7%
True 2888
31.3%
2023-07-16T16:55:06.402429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Last Notable Activity
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
Modified
3407 
Email Opened
2827 
SMS Sent
2172 
Page Visited on Website
 
318
Olark Chat Conversation
 
183
Other values (11)
 
333

Length

Max length28
Median length8
Mean length10.320996
Min length8

Characters and Unicode

Total characters95366
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowModified
2nd rowEmail Opened
3rd rowEmail Opened
4th rowModified
5th rowModified

Common Values

ValueCountFrequency (%)
Modified 3407
36.9%
Email Opened 2827
30.6%
SMS Sent 2172
23.5%
Page Visited on Website 318
 
3.4%
Olark Chat Conversation 183
 
2.0%
Email Link Clicked 173
 
1.9%
Email Bounced 60
 
0.6%
Unsubscribed 47
 
0.5%
Unreachable 32
 
0.3%
Had a Phone Conversation 14
 
0.2%
Other values (6) 7
 
0.1%

Length

2023-07-16T16:55:06.525087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
modified 3407
21.3%
email 3063
19.1%
opened 2827
17.6%
sms 2172
13.6%
sent 2172
13.6%
on 319
 
2.0%
website 319
 
2.0%
page 318
 
2.0%
visited 318
 
2.0%
conversation 197
 
1.2%
Other values (23) 910
 
5.7%

Most occurring characters

ValueCountFrequency (%)
e 13075
13.7%
i 11430
12.0%
d 10260
10.8%
6782
 
7.1%
S 6519
 
6.8%
n 6041
 
6.3%
M 5581
 
5.9%
o 4199
 
4.4%
a 4042
 
4.2%
l 3454
 
3.6%
Other values (27) 23983
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68557
71.9%
Uppercase Letter 20027
 
21.0%
Space Separator 6782
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13075
19.1%
i 11430
16.7%
d 10260
15.0%
n 6041
8.8%
o 4199
 
6.1%
a 4042
 
5.9%
l 3454
 
5.0%
f 3408
 
5.0%
t 3193
 
4.7%
m 3068
 
4.5%
Other values (11) 6387
9.3%
Uppercase Letter
ValueCountFrequency (%)
S 6519
32.6%
M 5581
27.9%
E 3063
15.3%
O 3010
15.0%
C 554
 
2.8%
P 332
 
1.7%
V 319
 
1.6%
W 319
 
1.6%
L 173
 
0.9%
U 79
 
0.4%
Other values (5) 78
 
0.4%
Space Separator
ValueCountFrequency (%)
6782
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88584
92.9%
Common 6782
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13075
14.8%
i 11430
12.9%
d 10260
11.6%
S 6519
 
7.4%
n 6041
 
6.8%
M 5581
 
6.3%
o 4199
 
4.7%
a 4042
 
4.6%
l 3454
 
3.9%
f 3408
 
3.8%
Other values (26) 20575
23.2%
Common
ValueCountFrequency (%)
6782
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13075
13.7%
i 11430
12.0%
d 10260
10.8%
6782
 
7.1%
S 6519
 
6.8%
n 6041
 
6.3%
M 5581
 
5.9%
o 4199
 
4.4%
a 4042
 
4.2%
l 3454
 
3.6%
Other values (27) 23983
25.1%

Interactions

2023-07-16T16:54:58.068503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:54.957620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.635606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.252577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.863383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.473210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:58.175218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.078341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.751323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.366273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.974544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.585909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:58.275741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.187797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.849654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.465009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.075274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.688634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:58.367496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.291557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.948391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.558758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.171019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.781385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:58.461258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.417191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.048152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.657494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.278730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.878127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:58.559722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:55.527919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.152843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:56.767579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.377465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-16T16:54:57.973492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-16T16:55:06.654513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Lead NumberTotalVisitsTotal Time Spent on WebsitePage Views Per VisitAsymmetrique Activity ScoreAsymmetrique Profile ScoreLead OriginLead SourceDo Not EmailDo Not CallConvertedLast ActivityCountrySpecializationHow did you hear about X EducationWhat is your current occupationWhat matters most to you in choosing a courseSearchNewspaper ArticleX Education ForumsNewspaperDigital AdvertisementThrough RecommendationsTagsLead QualityLead ProfileCityAsymmetrique Activity IndexAsymmetrique Profile IndexA free copy of Mastering The InterviewLast Notable Activity
Lead Number1.0000.0570.0310.066-0.075-0.1600.0870.1270.0990.0080.0870.0840.0250.0350.0430.0590.0000.0230.0000.0000.0110.0000.0000.0890.1120.1410.0280.1020.0770.1160.087
TotalVisits0.0571.0000.5860.850-0.2000.2340.0000.0140.0650.0000.0010.0170.0000.0470.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0160.031
Total Time Spent on Website0.0310.5861.0000.572-0.1680.2260.2100.1730.0630.0000.4260.0890.0000.0890.0700.0590.0000.0180.0250.0330.0480.0000.0410.1340.1400.0700.1190.1290.1530.2070.061
Page Views Per Visit0.0660.8500.5721.000-0.2580.2470.0830.1370.0450.0000.0000.0410.2140.0750.0000.0150.0000.0130.0160.0000.0000.0000.0140.0410.0060.0160.0650.0380.0380.1270.000
Asymmetrique Activity Score-0.075-0.200-0.168-0.2581.000-0.1320.1790.1740.1280.0000.4190.2380.0000.0380.0430.0620.0710.0000.0001.0001.0000.0000.0000.3150.1940.1340.0500.9990.1690.1480.096
Asymmetrique Profile Score-0.1600.2340.2260.247-0.1321.0000.4140.3080.0660.0000.2780.1110.0000.3070.1180.1260.0000.0350.0001.0001.0000.0000.0000.1230.2060.3490.4390.1810.9990.3730.056
Lead Origin0.0870.0000.2100.0830.1790.4141.0000.9020.1010.0140.3250.2010.0000.3600.2290.0860.0000.0010.0000.0000.0000.0000.0000.2770.1980.1110.3960.2040.4800.5690.103
Lead Source0.1270.0140.1730.1370.1740.3080.9021.0000.1390.0000.3360.1240.1120.1360.2080.1030.0000.0000.0000.0000.0000.0000.0000.1460.1870.1410.2480.2880.3820.6690.066
Do Not Email0.0990.0650.0630.0450.1280.0660.1010.1391.0000.0000.1350.6960.0930.1300.0540.0480.0000.0000.0000.0000.0000.0000.0000.2780.1860.0900.0780.0680.0000.0540.416
Do Not Call0.0080.0000.0000.0000.0000.0000.0140.0000.0001.0000.0040.0000.0000.0340.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Converted0.0870.0010.4260.0000.4190.2780.3250.3360.1350.0041.0000.3960.0070.0630.0510.3020.0000.0000.0000.0000.0000.0000.0100.9310.6590.3790.0720.1900.1730.0380.380
Last Activity0.0840.0170.0890.0410.2380.1110.2010.1240.6960.0000.3961.0000.0570.0770.0730.0760.0000.0000.0000.0000.0000.0000.0240.1620.2800.1250.1170.3150.1900.1970.686
Country0.0250.0000.0000.2140.0000.0000.0000.1120.0930.0000.0070.0571.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0290.0220.0660.0000.0000.0910.074
Specialization0.0350.0470.0890.0750.0380.3070.3600.1360.1300.0340.0630.0770.0191.0000.1280.1000.0000.0290.0290.0620.0000.0100.0000.0660.0910.1350.3370.0340.4910.4580.039
How did you hear about X Education0.0430.0750.0700.0000.0430.1180.2290.2080.0540.0430.0510.0730.0000.1281.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0500.0360.1870.0460.2220.3090.041
What is your current occupation0.0590.0000.0590.0150.0620.1260.0860.1030.0480.0000.3020.0760.0000.1000.0001.0000.0000.0000.0001.0000.0000.0000.0270.2070.2130.1950.0200.0660.1200.0230.060
What matters most to you in choosing a course0.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0000.0000.0000.0490.0360.0000.0000.0340.0000.0000.000
Search0.0230.0000.0180.0130.0000.0350.0010.0000.0000.0000.0000.0000.0000.0290.0000.0000.0001.0000.0930.1330.0000.0650.2520.0000.0000.0000.0130.0060.0060.0000.000
Newspaper Article0.0000.0000.0250.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0931.0000.3530.0000.1760.1330.0000.0000.0000.0230.0000.0000.0000.000
X Education Forums0.0000.0000.0330.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0620.0001.0001.0000.1330.3531.0000.0000.2500.1891.0001.0001.0000.0421.0001.0000.0000.000
Newspaper0.0110.0000.0480.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.000
Digital Advertisement0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0650.1760.2500.0001.0000.0930.0760.0000.0000.0190.0000.0110.0000.000
Through Recommendations0.0000.0000.0410.0140.0000.0000.0000.0000.0000.0000.0100.0240.0000.0000.0000.0270.0000.2520.1330.1890.0000.0931.0000.0000.0240.0000.0470.0000.0000.0040.058
Tags0.0890.0000.1340.0410.3150.1230.2770.1460.2780.0000.9310.1620.0330.0660.0460.2070.0490.0000.0001.0000.0000.0760.0001.0000.5420.3710.0760.4980.1630.1800.164
Lead Quality0.1120.0000.1400.0060.1940.2060.1980.1870.1860.0000.6590.2800.0290.0910.0500.2130.0360.0000.0001.0000.0000.0000.0240.5421.0000.4170.1220.1730.1430.1400.230
Lead Profile0.1410.0000.0700.0160.1340.3490.1110.1410.0900.0000.3790.1250.0220.1350.0360.1950.0000.0000.0001.0000.0000.0000.0000.3710.4171.0000.0900.1810.2300.0580.098
City0.0280.0000.1190.0650.0500.4390.3960.2480.0780.0000.0720.1170.0660.3370.1870.0200.0000.0130.0230.0420.0000.0190.0470.0760.1220.0901.0000.0460.4490.5040.065
Asymmetrique Activity Index0.1020.0280.1290.0380.9990.1810.2040.2880.0680.0000.1900.3150.0000.0340.0460.0660.0340.0060.0001.0001.0000.0000.0000.4980.1730.1810.0461.0000.1440.0960.170
Asymmetrique Profile Index0.0770.0000.1530.0380.1690.9990.4800.3820.0000.0000.1730.1900.0000.4910.2220.1200.0000.0060.0001.0001.0000.0110.0000.1630.1430.2300.4490.1441.0000.2590.098
A free copy of Mastering The Interview0.1160.0160.2070.1270.1480.3730.5690.6690.0540.0000.0380.1970.0910.4580.3090.0230.0000.0000.0000.0000.0000.0000.0040.1800.1400.0580.5040.0960.2591.0000.110
Last Notable Activity0.0870.0310.0610.0000.0960.0560.1030.0660.4160.0000.3800.6860.0740.0390.0410.0600.0000.0000.0000.0000.0000.0000.0580.1640.2300.0980.0650.1700.0980.1101.000

Missing values

2023-07-16T16:54:58.754332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T16:54:59.253582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-16T16:54:59.758264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Prospect IDLead NumberLead OriginLead SourceDo Not EmailDo Not CallConvertedTotalVisitsTotal Time Spent on WebsitePage Views Per VisitLast ActivityCountrySpecializationHow did you hear about X EducationWhat is your current occupationWhat matters most to you in choosing a courseSearchMagazineNewspaper ArticleX Education ForumsNewspaperDigital AdvertisementThrough RecommendationsReceive More Updates About Our CoursesTagsLead QualityUpdate me on Supply Chain ContentGet updates on DM ContentLead ProfileCityAsymmetrique Activity IndexAsymmetrique Profile IndexAsymmetrique Activity ScoreAsymmetrique Profile ScoreI agree to pay the amount through chequeA free copy of Mastering The InterviewLast Notable Activity
07927b2df-8bba-4d29-b9a2-b6e0beafe620660737APIOlark ChatNoNo00.000.0Page Visited on WebsiteNaNSelectSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoInterested in other coursesLow in RelevanceNoNoSelectSelect02.Medium02.Medium15.015.0NoNoModified
12a272436-5132-4136-86fa-dcc88c88f482660728APIOrganic SearchNoNo05.06742.5Email OpenedIndiaSelectSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoRingingNaNNoNoSelectSelect02.Medium02.Medium15.015.0NoNoEmail Opened
28cc8c611-a219-4f35-ad23-fdfd2656bd8a660727Landing Page SubmissionDirect TrafficNoNo12.015322.0Email OpenedIndiaBusiness AdministrationSelectStudentBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailMight beNoNoPotential LeadMumbai02.Medium01.High14.020.0NoYesEmail Opened
30cc2df48-7cf4-4e39-9de9-19797f9b38cc660719Landing Page SubmissionDirect TrafficNoNo01.03051.0UnreachableIndiaMedia and AdvertisingWord Of MouthUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoRingingNot SureNoNoSelectMumbai02.Medium01.High13.017.0NoNoModified
43256f628-e534-4826-9d63-4a8b88782852660681Landing Page SubmissionGoogleNoNo12.014281.0Converted to LeadIndiaSelectOtherUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailMight beNoNoSelectMumbai02.Medium01.High15.018.0NoNoModified
52058ef08-2858-443e-a01f-a9237db2f5ce660680APIOlark ChatNoNo00.000.0Olark Chat ConversationNaNNaNNaNNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNNaN01.High02.Medium17.015.0NoNoModified
69fae7df4-169d-489b-afe4-0f3d752542ed660673Landing Page SubmissionGoogleNoNo12.016402.0Email OpenedIndiaSupply Chain ManagementOnline SearchUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailLow in RelevanceNoNoPotential LeadMumbai02.Medium01.High14.020.0NoNoModified
720ef72a2-fb3b-45e0-924e-551c5fa59095660664APIOlark ChatNoNo00.000.0Olark Chat ConversationNaNNaNNaNNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNNaN02.Medium02.Medium15.015.0NoNoModified
8cfa0128c-a0da-4656-9d47-0aa4e67bf690660624Landing Page SubmissionDirect TrafficNoNo02.0712.0Email OpenedIndiaIT Projects ManagementNaNNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNThane & Outskirts02.Medium02.Medium14.014.0NoYesEmail Opened
9af465dfc-7204-4130-9e05-33231863c4b5660616APIGoogleNoNo04.0584.0Email OpenedIndiaFinance ManagementWord Of MouthNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNMumbai02.Medium02.Medium13.016.0NoNoEmail Opened
Prospect IDLead NumberLead OriginLead SourceDo Not EmailDo Not CallConvertedTotalVisitsTotal Time Spent on WebsitePage Views Per VisitLast ActivityCountrySpecializationHow did you hear about X EducationWhat is your current occupationWhat matters most to you in choosing a courseSearchMagazineNewspaper ArticleX Education ForumsNewspaperDigital AdvertisementThrough RecommendationsReceive More Updates About Our CoursesTagsLead QualityUpdate me on Supply Chain ContentGet updates on DM ContentLead ProfileCityAsymmetrique Activity IndexAsymmetrique Profile IndexAsymmetrique Activity ScoreAsymmetrique Profile ScoreI agree to pay the amount through chequeA free copy of Mastering The InterviewLast Notable Activity
9230d11c15b7-8056-45a6-8954-771c0d0495fe579701Landing Page SubmissionGoogleNoNo02.08702.00Email OpenedIndiaHuman Resource ManagementOnline SearchUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailNaNNoNoPotential LeadMumbai02.Medium01.High13.020.0NoNoEmail Opened
92314aeae36b-2b57-494f-bdab-dd58844286b4579697Landing Page SubmissionGoogleNoNo18.010164.00Email OpenedIndiaBanking, Investment And InsuranceOnline SearchUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailHigh in RelevanceNoNoPotential LeadMumbai02.Medium01.High15.020.0NoNoEmail Opened
92322d0109e9-dfb2-4664-83de-c2ea75ec7516579642Landing Page SubmissionDirect TrafficNoNo02.017702.00SMS SentIndiaHuman Resource ManagementSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoRingingNot SureNoNoPotential LeadMumbai02.Medium01.High14.020.0NoYesSMS Sent
92333f715465-2546-47cd-afa8-8b8dc63b8b43579622APIDirect TrafficNoNo113.014092.60SMS SentIndiaSelectSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailNaNNoNoSelectSelectNaNNaNNaNNaNNoNoSMS Sent
9234c0b25922-511f-4c56-852e-ced210a45447579615Landing Page SubmissionDirect TrafficNoNo15.02102.50SMS SentIndiaBusiness AdministrationSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailMight beNoNoPotential LeadMumbai02.Medium01.High14.020.0NoNoModified
923519d6451e-fcd6-407c-b83b-48e1af805ea9579564Landing Page SubmissionDirect TrafficYesNo18.018452.67Email Marked SpamSaudi ArabiaIT Projects ManagementSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailHigh in RelevanceNoNoPotential LeadMumbai02.Medium01.High15.017.0NoNoEmail Marked Spam
923682a7005b-7196-4d56-95ce-a79f937a158d579546Landing Page SubmissionDirect TrafficNoNo02.02382.00SMS SentIndiaMedia and AdvertisingSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNowrong number givenMight beNoNoPotential LeadMumbai02.Medium01.High14.019.0NoYesSMS Sent
9237aac550fe-a586-452d-8d3c-f1b62c94e02c579545Landing Page SubmissionDirect TrafficYesNo02.01992.00SMS SentIndiaBusiness AdministrationSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoinvalid numberNot SureNoNoPotential LeadMumbai02.Medium01.High13.020.0NoYesSMS Sent
92385330a7d1-2f2b-4df4-85d6-64ca2f6b95b9579538Landing Page SubmissionGoogleNoNo13.04993.00SMS SentIndiaHuman Resource ManagementOnline SearchNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNOther Metro Cities02.Medium02.Medium15.016.0NoNoSMS Sent
9239571b5c8e-a5b2-4d57-8574-f2ffb06fdeff579533Landing Page SubmissionDirect TrafficNoNo16.012793.00SMS SentBangladeshSupply Chain ManagementSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailMight beNoNoPotential LeadOther Cities02.Medium01.High15.018.0NoYesModified